Neural Fake Factor Estimation Using Data-Based Inference

TL;DR

This paper presents a neural network-based data-driven method for estimating fake backgrounds in high-energy physics, offering more precise and flexible fake factor calculations than traditional histogram-based techniques.

Contribution

The authors introduce a novel neural density ratio estimation approach for fake factor calculation, enabling continuous, high-dimensional, event-by-event estimates in physics analyses.

Findings

Neural network method produces smoother fake factor estimates.

The approach reduces binning artifacts compared to traditional methods.

Demonstrated feasibility on LHC open data.

Abstract

In a high-energy physics data analysis, the term "fake" backgrounds refers to events that would formally not satisfy the (signal) process selection criteria, but are accepted nonetheless due to mis-reconstructed particles. This can occur, e.g., when leptons from secondary decays are incorrectly identified as originating from the hard-scatter interaction point (known as non-prompt leptons), or when other physics objects, such as hadronic jets, are mistakenly reconstructed as leptons (resulting in mis-identified leptons). These fake leptons are usually estimated using data-driven techniques, one of the most common being the Fake Factor method. This method relies on predicting the fake lepton contribution by reweighting data events, using a scale factor (i.e. fake factor) function. Traditionally, fake factors have been estimated by histogramming and computing the ratio of two data distributions, typically as functions of a few relevant physics variables such as the transverse momentum $p_\text{T}$ and pseudorapidity $\eta$. In this work, we introduce a novel approach of fake factor calculation, based on density ratio estimation using neural networks trained directly on data in a higher-dimensional feature space. We show that our method enables the computation of a continuous, unbinned fake factor on a per event basis, offering a more flexible, precise, and higher-dimensional alternative to the conventional method, making it applicable to a wide range of analyses. A simple LHC open data analysis we implemented confirms the feasibility of the method and demonstrates that the ML-based fake factor provides smoother, more stable estimates across the phase space than traditional methods, reducing binning artifacts and improving extrapolation to signal regions.